Synopsis

The availability of inexpensive GPU-based compute has opened the door to new strategies for the acquisition and the reconstruction of highly-undersampled imaging data. We have been developing neural network deep learning based approaches such as AUTOMAP and to leverage scalable-compute and significantly reduce the need for precision scanning hardware. These approaches are very valuable in low SNR regimes like millitesla MRI or high-b value DWI. We describe here the AUTOMAP formalism and how it can be used to improve reconstruction SNR and accuracy as well as open up the possibility of new sampling strategies.